Multi-variate model for predicting cytokine release syndrome

Abstract

Techniques are provided for predicting a risk of a subject experiencing a cytokine release syndrome of at least a threshold grade subsequent to receiving a treatment. The risk may be predicted based on (for example) a set of baseline characteristics, a risk-score generation model, an on-treatment cytokine level, and/or a treatment dosage. The risk may be used to generate an output corresponding to a recommendation as to whether to monitor the subject via in-patient monitoring.

1 . A computer-implemented method comprising:
 identifying a set of baseline characteristics of a subject who has been diagnosed with cancer, wherein the set of baseline characteristics pertain to one or more baseline time points that are before initiation of a treatment, and wherein each of the set of baseline characteristics characterize:
 a stage of the cancer; 
 a demographic attribute; 
 a size of one or more tumors; 
 a white blood cell count; and/or 
 a lactate dehydrogenase level; 
   generating a numeric cytokine release syndrome risk score by processing the set of baseline characteristics using a risk-score generation model;   predicting, based on the numeric cytokine release syndrome risk score, a risk of the subject experiencing a cytokine release syndrome of at least a threshold grade subsequent to receiving the treatment;   determining a result based on the predicted risk corresponding to a recommendation as to whether to monitor the subject via in-patient monitoring subsequent to completion of the treatment; and   outputting the result.   
     
     
         2 . The computer-implemented method of  claim 1 , wherein at least one of the set of baseline characteristics characterizes a stage of the cancer. 
     
     
         3 . The computer-implemented method of  claim 1 , wherein at least one of the set of baseline characteristics characterizes a lactate dehydrogenase level. 
     
     
         4 . The computer-implemented method of  claim 1 , wherein at least one of the set of baseline characteristics characterizes a white blood cell count. 
     
     
         5 . The computer-implemented method of  claim 1 , wherein at least one of the set of baseline characteristics characterizes a size of the one or more tumors. 
     
     
         6 . The computer-implemented method of  claim 1 , wherein at least one of the set of baseline characteristics characterizes a demographic attribute. 
     
     
         7 . The computer-implemented method of  claim 1 , further comprising:
 determining the result based on the predicted risk, wherein the result corresponds to a recommendation as to whether to monitor the subject via in-patient monitoring subsequent to completion of the treatment.   
     
     
         8 . The computer-implemented method of  claim 1 , further comprising:
 identifying an on-treatment level of a cytokine, wherein the on-treatment level of the cytokine indicates a level of the cytokine in an on-treatment sample collected from the subject while the treatment was being administered or within an hour of completion of the treatment;   determining an on-treatment cytokine fold change of the cytokine based on the on-treatment level of the cytokine and a baseline level of the cytokine that indicates a level of the cytokine in a baseline sample collected from the subject before initiation of the treatment;   wherein the predicted risk is further based on the on-treatment cytokine fold change.   
     
     
         9 . The computer-implemented method of  claim 1 , further comprising:
 identifying a dosage of at least prat of the treatment, wherein the predicted risk is further based on the dosage.   
     
     
         10 . The computer-implemented method of  claim 1 , wherein the risk-score generation includes a regression model. 
     
     
         11 . The computer-implemented method of  claim 1 , wherein the treatment includes administering a T cell immunotherapy. 
     
     
         12 . The computer-implemented method of  claim 1 , wherein the treatment includes administering glofitamab or mosunetuzumab. 
     
     
         13 . The computer-implemented method of  claim 1 , wherein the treatment comprises administering a therapy that comprises an antibody or a small molecule. 
     
     
         14 . The computer-implemented method of  claim 13 , wherein the administered therapy comprises an antibody. 
     
     
         15 . The computer-implemented method of  claim 14 , wherein the antibody is a multispecific antibody that engages T-cells when bound to at least one of its antigens. 
     
     
         16 . A computer-implemented method comprising:
 identifying an on-treatment level of a cytokine, wherein the on-treatment level of the cytokine indicates a level of the cytokine in an on-treatment sample collected from a subject while a treatment was being administered or within an hour of completion of the treatment;   determining an on-treatment cytokine fold change of the cytokine based on the on-treatment level of the cytokine and a baseline level of the cytokine that indicates a level of the cytokine in a baseline sample collected from the subject before initiation of the treatment;   identifying a dosage of at least part of the treatment;   predicting, based on the on-treatment cytokine fold change and the dosage, a risk of the subject experiencing a cytokine release syndrome of at least a threshold grade subsequent to receiving the dosage of the at least part of the treatment;   determining a result based on the predicted risk corresponding to a recommendation as to whether to monitor the subject via in-patient monitoring subsequent to completion of the treatment; and   outputting the result.   
     
     
         17 . The computer-implemented method of  claim 16 , further comprising:
 identifying a set of baseline characteristics of the subject, wherein the set of baseline characteristics pertain to one or more baseline time points that are before the initiation of the treatment, and wherein each of the set of baseline characteristics characterize:
 a tumor burden; 
 a stage of cancer; 
 a tumor spread; 
 a size of one or more tumors; 
 a demographic attribute; 
 a white blood cell count; and/or 
 a lactate dehydrogenase level; 
   wherein the predicted risk further depends on the set of baseline characteristics.   
     
     
         18 . The computer-implemented method of  claim 17 , further comprising:
 generating a cytokine release syndrome risk score by processing the set of baseline characteristics with a risk-score generation model, wherein the predicted risk is based on the cytokine release syndrome risk score.   
     
     
         19 . The computer-implemented method of  claim 18 , wherein the risk-score generation includes a regression model. 
     
     
         20 . The computer-implemented method of  claim 18 , wherein the one or more parameters include a set of weights. 
     
     
         21 . The computer-implemented method of  claim 18 , wherein the risk is determined based on a linear combination of the cytokine release syndrome risk score and the dosage. 
     
     
         22 . The computer-implemented method of  claim 16 , wherein predicting the risk that the subject will experience the cytokine release syndrome includes performing one or more threshold comparisons. 
     
     
         23 . The computer-implemented method of  claim 16 , wherein the subject has been diagnosed with cancer, and wherein the treatment includes administering a T cell immunotherapy. 
     
     
         24 . The computer-implemented method of  claim 16 , wherein the subject has been diagnosed with cancer, and wherein the treatment includes administering glofitamab or mosunetuzumab. 
     
     
         25 . The computer-implemented method of  claim 16 , wherein determining the on-treatment cytokine fold change of the cytokine based on the baseline level of the cytokine includes:
 calculating a log of the baseline level of the cytokine or of a processed version thereof to generate a baseline log value;   calculating a log of the on-treatment level of the cytokine or a processed version thereof to generate an on-treatment log value; and   subtracting the baseline log value from the on-treatment log value.   
     
     
         26 . The computer-implemented method of  claim 16 , wherein determining the on-treatment cytokine fold change of the cytokine based on the baseline level of the cytokine includes:
 calculating a log of a difference between the baseline level of the cytokine and a constant to generate a baseline log value;   calculating a log of a difference between the on-treatment level of the cytokine and the constant to generate an on-treatment log value; and   subtracting the baseline log value from the on-treatment log value.   
     
     
         27 . The computer-implemented method of  claim 16 , wherein identifying the on-treatment level of the cytokine includes:
 identifying multiple preliminary on-treatment levels of the cytokine that indicate levels of the cytokine in multiple on-treatment samples collected from the subject while the treatment was being administered or within a day of completion of the treatment, wherein each of the multiple on-treatment samples was collected at a different time; and   defining the on-treatment level of the cytokine to be a maximum of the multiple preliminary on-treatment levels of the cytokine.   
     
     
         28 . The computer-implemented method of  claim 16 , wherein:
 the treatment includes administering an active ingredient; and   the treatment was preceded by administering a pre-treatment with another agent.   
     
     
         29 . The computer-implemented method of  claim 28 , wherein the on-treatment level was identified using a sample collected after administration of the active ingredient. 
     
     
         30 . The computer-implemented method of  claim 16 , wherein the cytokine includes Tumor Necrosis Factor alpha, Interleukin 6, Interleukin 8, Interleukin 10, or Macrophage Inflammatory Protein 1 beta. 
     
     
         31 . The computer-implemented method of  claim 29 , wherein the treatment comprises administering a therapy that comprises an antibody or a small molecule. 
     
     
         32 . A system comprising:
 one or more data processors; and   a non-transitory computer readable storage medium containing instructions which, when executed on the one or more data processors, cause the one or more data processors to perform the method of any of  claims 1 - 31 .   
     
     
         33 . A computer-program product tangibly embodied in a non-transitory machine-readable storage medium, including instructions configured to cause one or more data processors to perform the method any of  claims 1 - 31 .